Explore the recent global developments with R

Today, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.

Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks below.

Get the necessary packages

First, start with installing the relevant packages ‘tidyverse’, ‘gganimate’, and ‘gapminder’.

Look at the data

First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.

year_list <- unique(gapminder$year)
head(gapminder)

## # A tibble: 6 x 6
##   country     continent  year lifeExp      pop gdpPercap
##   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan Asia       1952    28.8  8425333      779.
## 2 Afghanistan Asia       1957    30.3  9240934      821.
## 3 Afghanistan Asia       1962    32.0 10267083      853.
## 4 Afghanistan Asia       1967    34.0 11537966      836.
## 5 Afghanistan Asia       1972    36.1 13079460      740.
## 6 Afghanistan Asia       1977    38.4 14880372      786.

#putting the data in my environment
gapminder <- gapminder

The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.

Let’s plot all the countries in 1952.

theme_set(theme_bw())  # set theme to white background for better visibility

ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10() +
  labs(title = "1952 plot",
       x = 'Gross domestic product (GDP) per capita',
       y = 'Life expectancy',
       size = 'Population')

#Calculating max and min values og gdp
range(gapminder$gdpPercap)

## [1]    241.1659 113523.1329

#Getting the richest country in year 1952
gapminder %>% 
  filter(year == 1952) %>%
  arrange(desc(gdpPercap))

## # A tibble: 142 x 6
##    country        continent  year lifeExp       pop gdpPercap
##    <fct>          <fct>     <int>   <dbl>     <int>     <dbl>
##  1 Kuwait         Asia       1952    55.6    160000   108382.
##  2 Switzerland    Europe     1952    69.6   4815000    14734.
##  3 United States  Americas   1952    68.4 157553000    13990.
##  4 Canada         Americas   1952    68.8  14785584    11367.
##  5 New Zealand    Oceania    1952    69.4   1994794    10557.
##  6 Norway         Europe     1952    72.7   3327728    10095.
##  7 Australia      Oceania    1952    69.1   8691212    10040.
##  8 United Kingdom Europe     1952    69.2  50430000     9980.
##  9 Bahrain        Asia       1952    50.9    120447     9867.
## 10 Denmark        Europe     1952    70.8   4334000     9692.
## # … with 132 more rows

We see an interesting spread with an outlier to the right. Answer the following questions, please:

Q1. Why does it make sense to have a log10 scale on x axis? Because the distance in the gdp per capita variable from the richest to the next richest data point varies by such a big increment (it jumps from 14.734 to 113.523) it would not look good to have all data points crammed in one side of the plot. The logarithmic scale solves this as it is not linearly scaled it looks more continuous (thus there will be the same distance between 0-1.000, 1.000-10.000 and 10.000 -100.000).

Q2. What country is the richest in 1952 (far right on x axis)? The data organisations tells us that from this data-set, Kuwait is the richest country with 108.382 gdp per capita in the year 1952 (with life expectancy of 55.6 years and a population size of 160.000 people)

You can generate a similar plot for 2007 and compare the differences

ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10()

The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.

Q3. Can you differentiate the continents by color and fix the axis labels?

ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop, color = continent)) + #adding color=continent to get continents colored
  geom_point() +
  scale_x_log10() +
  labs(title = "Gapminder: 2007 plot",
       x = 'Gross domestic product (GDP) per capita',
       y = 'Life expectancy',
       size = 'Population') #using labs to change labels on the axis and put on a title

Q4. What are the five richest countries in the world in 2007?

gapminder %>% 
  filter(year == 2007) %>%
  arrange(desc(gdpPercap)) %>% 
  head(5)

## # A tibble: 5 x 6
##   country       continent  year lifeExp       pop gdpPercap
##   <fct>         <fct>     <int>   <dbl>     <int>     <dbl>
## 1 Norway        Europe     2007    80.2   4627926    49357.
## 2 Kuwait        Asia       2007    77.6   2505559    47307.
## 3 Singapore     Asia       2007    80.0   4553009    47143.
## 4 United States Americas   2007    78.2 301139947    42952.
## 5 Ireland       Europe     2007    78.9   4109086    40676.

Rewriting some code from above I used ‘filter’ to get data only from the year 2007, then I used ‘arrange’ with the argument ‘desc’ to get it in descendant order (with the biggest numbers first), and then ‘head’ to show only the first 5 rows. It tells me that the 5 richest countries in 2007, according to this dataset, are: Norway, Kuwait, Singapore, United States and Ireland.

Make it move!

The comparison would be easier if we had the two graphs together, animated. We have a lovely tool in R to do this: the gganimate package. And there are two ways of animating the gapminder ggplot.

Option 1: Animate using transition_states()

The first step is to create the object-to-be-animated

anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent)) +
  geom_point() +
  scale_size_continuous(label=comma) +
  scale_x_log10(labels = scales::comma) +  # convert x to log scale
  labs(title = "Gapminder: animated plot",
       x = 'Gross domestic product (GDP) per capita',
       y = 'Life expectancy',
       size = 'Population',
       color = 'Continent')
anim

This plot collates all the points across time. The next step is to split it into years and animate it. This may take some time, depending on the processing power of your computer (and other things you are asking it to do). Beware that the animation might appear in the ‘Viewer’ pane, not in this rmd preview. You need to knit the document to get the viz inside an html file.

anim + transition_states(year, 
                      transition_length = 1,
                      state_length = 1)

Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.

Option 2 Animate using transition_time()

This option smoothes the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.

anim2 <- anim +
  transition_time(year)
anim2

The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.

Q5 Can you add a title to one or both of the animations above that will change in sync with the animation? [hint: search labeling for transition_states() and transition_time() functions respectively]

anim3 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent)) +
  geom_point() +
  scale_x_log10() +  # convert x to log scale
  labs(title = "Year: {frame_time}",
       x = 'Gross domestic product (GDP) per capita',
       y = 'Life expectancy',
       size = 'Population') +
  transition_time(year) 

anim3

I used this tutorial to make the animation: https://anderfernandez.com/en/blog/how-to-create-animations-in-r-with-gganimate/ I found out that I can use the {frame_time} together with ‘transition_time’ to make it change for each new frame. But… I also found a nicer way visualising the dates from the tutorial:

anim4 <- anim +
  geom_text(aes(x = min(gdpPercap), y = min(lifeExp), label = as.factor(year)) , hjust=-1, vjust = -0.1, alpha = 0.2,  col = "gray", size = 20) +
  transition_states(as.factor(year), state_length = 0)

anim4

Now the numbers appear inside the graph and I can keep the title I had chosen before.

Q6 Can you made the axes’ labels and units more readable? Consider expanding the abreviated lables as well as the scientific notation in the legend and x axis to whole numbers.[hint:search disabling scientific notation]

anim + 
  scale_size_continuous(label = comma) + #Scales the population size to full numbers
  scale_x_log10(labels = comma) #Scales the GDP per capita values on the x-axis to full numbers

## Scale for 'size' is already present. Adding another scale for 'size',
## which will replace the existing scale.

## Scale for 'x' is already present. Adding another scale for 'x', which
## will replace the existing scale.

Inspiration for solving this problem was found with a google search which led me here: https://stackoverflow.com/questions/32272522/removing-scientific-notation-from-a-ggplot-map-legend I installed the package ‘scales’: https://github.com/r-lib/scales I then used the ‘scale_size_continuous’ to get the numbers in the label on the side where it displays population comma-seperated. And finally used the ‘labels’ function in the x-axis scaling to also get the numbers on the x-axis comma-sperated.

Q7 Come up with a question you want to answer using the gapminder data and write it down. Then, create a data visualisation that answers the question and explain how your visualization answers the question. (Example: you wish to see what was mean life expectancy across the continents in the year you were born versus your parents’ birth years). [hint: if you wish to have more data than is in the filtered gapminder, you can load either the gapminder_unfiltered dataset and download more at https://www.gapminder.org/data/ ]

#Gapminder research question Does pollusion go up as people get more money? Does co2 emission per capita rise with income per capita?

#loading an extented version of Gapminder
gapminder2 <- gapminder_unfiltered
#loading data of co2 emission and population
co2_emissions_tonnes_per_person <- read_csv("Gapminder_data/co2_emissions_tonnes_per_person.csv")

## Parsed with column specification:
## cols(
##   .default = col_double(),
##   country = col_character()
## )

## See spec(...) for full column specifications.

#making the dataframe into long format
country_list <- co2_emissions_tonnes_per_person$country
column_list <- colnames(co2_emissions_tonnes_per_person)
column_list <- column_list[column_list != 'country']
co2_emissions_tonnes_per_person <- co2_emissions_tonnes_per_person %>% 
  gather(key = "year", value = "co2_emission", column_list)

## Note: Using an external vector in selections is ambiguous.
## ℹ Use `all_of(column_list)` instead of `column_list` to silence this message.
## ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This message is displayed once per session.

#merging the to datasets 
gapminder_co2 <- merge(gapminder2, co2_emissions_tonnes_per_person, by = c('year', 'country'))

#ommitting rows with NA's
gapminder_co2_complete <- drop_na(gapminder_co2, co2_emission)

Answering the question with an animated object

#Generating the anim object
anim_co2 <- ggplot(gapminder_co2_complete, aes(gdpPercap, co2_emission, size = pop, color = continent)) +
  geom_point() +
  scale_size_continuous(label=comma) +
  scale_x_log10(labels = scales::comma) +  # convert x to log scale
  scale_y_log10(labels = scales::comma) +  # convert y to log scale
  labs(title = "Does polusion rise as people become richer?",
       subtitle = "The whole world",
       x = 'Gross domestic product (GDP) per capita',
       y = 'CO2 emission in tons per capita',
       size = 'Population',
       color = 'Continent',
       caption = 'Data source: www.gapminder.org/data/')+
  geom_text(aes(x = min(gdpPercap), y = min(lifeExp), label = as.factor(year)), hjust=-1, vjust = 8, alpha = 0.2,  col = "gray", size = 20) +
  transition_states(as.factor(year), state_length = 0)
anim_co2

## Warning: Transformation introduced infinite values in continuous y-axis

## Warning: Removed 2 rows containing missing values (geom_point).

#Looking at Europe only
europe <- gapminder_co2_complete %>%
  filter(continent =="Europe") %>% 
  ggplot(aes(gdpPercap, co2_emission, size = pop, color = country)) +
  geom_point() +
  geom_text(aes(label=country), hjust=0.1, vjust=-0.9, nudge_x = -.04)+ #adjustes the placement of country labels
  theme(legend.position = "none") + #removes the caption of countries from the sidebar
  scale_size_continuous(label=comma) +
  scale_x_log10(labels = scales::comma) +  # convert x to log scale
  labs(title = "Does polusion rise as people become richer?",
       subtitle = "Only Europe",
       x = 'Gross domestic product (GDP) per capita',
       y = 'CO2 emission in tons per capita',
       size = 'Population',
       caption = 'Data source: www.gapminder.org/data/') +
  geom_text(aes(x = min(gdpPercap), y = min(co2_emission), label = as.factor(year)), hjust=0.1, vjust = -9, alpha = 0.2,  col = "gray", size = 20) +
  transition_states(as.factor(year), state_length = 0)
europe

Looking at Europe it seems that some countries are worse than others. I want to look at how the worst are comparing to each other by animating a bar chart race inspired by the tutorial mentioned earlier:

##Barchart race
#filtering the 15 most polluting countries in Europe
gapminder_europe_most_co2 <- gapminder_co2 %>% 
  filter(continent =="Europe") %>% 
  group_by(year) %>%
  arrange(year, desc(co2_emission)) %>%
  mutate(ranking = row_number()) %>%
  filter(ranking <=15)
head(gapminder_europe_most_co2)

## # A tibble: 6 x 8
## # Groups:   year [1]
##    year country     continent lifeExp    pop gdpPercap co2_emission ranking
##   <int> <fct>       <fct>       <dbl>  <int>     <dbl>        <dbl>   <int>
## 1  1950 Luxembourg  Europe       65.7 2.96e5    14555.        25.1        1
## 2  1950 United Kin… Europe       69.0 5.01e7     9767.         9.9        2
## 3  1950 Belgium     Europe       66.4 8.64e6     7990.         8.83       3
## 4  1950 Germany     Europe       66.5 6.84e7     6090.         7.31       4
## 5  1950 Czech Repu… Europe       64.4 8.93e6     6691.         6.51       5
## 6  1950 Slovak Rep… Europe       60.9 3.46e6     4938.         5.39       6

#Creating the bar chart race
anim_race <- gapminder_europe_most_co2 %>%
  ggplot() +
  geom_col(aes(ranking, co2_emission, fill = country)) +
  labs(title = "Bar race of top ranked CO2 emission",
       subtitle = "Only Europe",
       x = 'CO2 emission in tons per capita',
       caption = 'Data source: www.gapminder.org/data/')+
  geom_text(aes(ranking, co2_emission, label = 'CO2 emission'), hjust=-0.1) +
  geom_text(aes(ranking, y=0 , label = country), hjust=1.1) + 
  geom_text(aes(x=15, y=max(co2_emission) , label = as.factor(year)), vjust = 0.2, alpha = 0.5,  col = "gray", size = 20) +
  coord_flip(clip = "off", expand = FALSE) + scale_x_reverse() +
  theme_minimal() + theme(
    panel.grid = element_blank(), 
    legend.position = "none",
    axis.ticks.y = element_blank(),
    axis.title.y = element_blank(),
    axis.text.y = element_blank(),
    plot.margin = margin(1, 4, 1, 3, "cm")
  ) +
  transition_states(year, state_length = 0, transition_length = 6) +
  enter_fade() +
  exit_fade() + 
  ease_aes('quadratic-in-out') 
anim_race

It looks like some data is missing for some years for some countries, which looks strange in the animations, so I will try to create an animation where only the countries with a full dataset appears

#only countries with a full data-set
complete_countries <- gapminder_co2_complete %>% 
  group_by(country) %>% 
  filter(n()==58)
#counting which countries that have complete data
complete_country_list <- unique(complete_countries$country)
#looking at which countries remain
unique(complete_countries$country)

##  [1] Czech Republic  Denmark         Finland         Iceland        
##  [5] Japan           Netherlands     Norway          Portugal       
##  [9] Slovak Republic Spain           Sweden          Switzerland    
## 187 Levels: Afghanistan Albania Algeria Angola Argentina Armenia ... Zimbabwe

It looks like only 13 countries are left…

I will try to make an animation of the countries which are left.

#make animation
anim_co2_complete <- 
  complete_countries %>% 
  ggplot(aes(gdpPercap, co2_emission, color = country)) +
  theme_minimal() +
  geom_point() +
  geom_text(aes(label=country), hjust=-0.1, vjust=-0.6, nudge_x = -.04)+ 
  scale_size_continuous(label=comma) +
  scale_x_log10(labels = scales::comma) +  # convert x to log scale
  #scale_y_log10(labels = scales::comma) +  
  theme(legend.position = "none") +
  labs(title = "Does polusion rise as people become richer?",
       subtitle = "The 13 remaining countris",
       x = 'Gross domestic product (GDP) per capita',
       y = 'CO2 emission in tons per capita',
       caption = 'Data source: www.gapminder.org/data/') +
  geom_text(aes(x = min(gdpPercap), y = min(co2_emission), label = as.factor(year)), hjust=0.1, vjust = -7, alpha = 0.2,  col = "gray", size = 20) +
  transition_states(as.factor(year), state_length = 0)
anim_co2_complete

Even though it looks nicer, it is not so respresentative and I don’t know what to say about the patterns in general.

So my conclusion to the question is, that Luxembourg is far the most pulluting country, but also quite rich, so their might be a correlation here. But since there is a lot of missing data, it is hard to say much about the general pattern, even though it quite clearly seems that CO2 emission and income is following each other quite linearly and that both has gone up quite a lot from 1950 to 2007.

HW5 - Make Data Move

Matilde Jacobsen